{"id":"W4409795196","doi":"10.61091/jcmcc127b-523","title":"Optimization of Random Forest Algorithm and Research on the Effectiveness of its Application in Stock Index Forecasting","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Random forest; Index (typography); Stock (firearms); Optimization algorithm; Algorithm; Computer science; Mathematics; Artificial intelligence; Mathematical optimization; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.05154724,0.0001796765,0.0008934907,0.0008929946,0.0002274689,0.000131486,0.000625027,0.0001555781,0.000001934598],"category_scores_gemma":[0.028771,0.0001232285,0.000113757,0.00181123,0.000198554,0.0001447233,0.0003667902,0.0005817571,1.417879e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007960798,"about_ca_system_score_gemma":0.0001644838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001273355,"about_ca_topic_score_gemma":6.03842e-7,"domain_scores_codex":[0.9936447,0.002437942,0.001815562,0.0002704167,0.00158206,0.0002492926],"domain_scores_gemma":[0.9458499,0.05020456,0.001485759,0.0003054835,0.002090425,0.00006388373],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002401059,0.0008121828,0.01288969,0.0006943286,0.0001219015,0.000005492374,0.001123051,0.02472741,0.0005088394,0.8957095,0.00005309452,0.06095347],"study_design_scores_gemma":[0.003657819,0.0003974371,0.002125912,0.001085633,0.00001939305,0.000008901983,0.0003281908,0.4490032,0.0009447522,0.5423434,0.00001327569,0.00007197136],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.715036,0.0001645299,0.2802005,0.00005694065,0.002857468,0.0007800263,0.00000134504,0.000004446156,0.0008987022],"genre_scores_gemma":[0.9893155,0.00001354946,0.01049337,0.000003085008,0.0001493934,0.000008058199,2.612617e-7,0.00001280879,0.000004041193],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4242758,"threshold_uncertainty_score":0.9794101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08360822677457928,"score_gpt":0.3996262695310554,"score_spread":0.3160180427564761,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}